Robotics Autonomy Engineer - Locomotion

Position: 1.51 Robotics Autonomy Engineer - Locomotion
Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.

About the Job:

Field AI is building the future of autonomy-from rugged terrain to real-world deployment. We're on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments. As our Robotics Autonomy Engineer - Locomotion
, you'll lead the development and deployment of state-of-the-art reinforcement learning-based controllers for legged and humanoid robots. You'll be part of a deeply technical team advancing real-world robotic capabilities through cutting-edge research, simulation tools, and field validation.

If designing locomotion systems that can navigate complex, dynamic environments excites you, and you want to work where your code hits the ground (literally)-this is your role. This is Field AI.

What You'll Get To Do

1. Design and Own RL-Based Locomotion Pipelines
• Architect and implement scalable reinforcement learning (RL) pipelines optimized for locomotion and manipulation.
• Integrate physics-based simulation environments (Isaac Gym, Isaac Lab, Mu Jo Co ) with custom training workflows.
• Develop reward functions, policy architectures, and domain randomization strategies that close the sim-to-real gap.
2. Deploy Locomotion Behaviors on Physical Robots
• Create agile, robust locomotion behaviors for quadruped and humanoid platforms, and validate them on real hardware.
• Solve real-world challenges in balance, contact-rich dynamics, high-DOF coordination, and terrain variability.
• Drive iterative testing across terrain variability and unstructured environments.
3. Build and Scale Simulation Infrastructure
• Build scalable training environments using GPU-accelerated simulators.
• Automate evaluation across domain-randomized scenarios and domain adaptation protocols.
• Maintain simulation infrastructure that enables rapid prototyping, validation, and reproducibility.
4. Collaborate Across the Full Robotics Stack
• Work closely with systems engineers, perception experts, and embedded teams to close the loop between learning and execution.
• Incorporate real-world telemetry and field data to continuously improve model generalization.
• Lead deployment workflows from experiment through lab testing to field robot validation.

What You Have

• Master's degree or higher in Robotics, Computer Science, Engineering, or related field (PhD strongly preferred).
• Deep expertise in reinforcement learning for continuous control.
• 2+ years of experience developing and deploying locomotion policies on real robotic systems.
• Hands-on experience with legged robot platforms (quadrupeds, bipedal systems, or exoskeletons).
• Proficiency with simulation tools such as Isaac Gym, Isaac Lab, Mu Jo Co , or PyBullet.
• Strong command of sim-to-real transfer - domain randomization, system identification, adaptive methods, and a track record of bridging the gap successfully.
• Solid understanding of contact dynamics, control theory, and kinematics.
• Strong Python and/or C++ development skills in Linux-based development environments.
• Familiarity with machine learning frameworks (PyTorch, Tensor Flow).
• A passion for building things that move in the real world.

The Extras That Set You Apart

• 3+ years of experience in an industry or startup robotics setting.
• Experience deploying neural network controllers on resource-constrained robotic platforms (real-time inference, onboard compute).
• Publications or open-source contributions in locomotion, RL, or control.
• Familiarity with ROS/ROS2 or custom middleware for real-time control.
• Background in manipulation, loco-manipulation, or whole-body coordination.
• Experience debugging sim-to-real issues at scale.
• Contributions to reinforcement learning libraries or simulation platforms.
• Prior work on…